Journal of Environmental Management 131 (2013) 325e333
Contents lists available at ScienceDirect
Journal of Environmental Management journal homepage: www.elsevier.com/locate/jenvman
Examining the relative effects of fire weather, suppression and fuel treatment on fire behaviour e A simulation study T.D. Penman a, *, L. Collins a, O.F. Price a, R.A. Bradstock a, S. Metcalf a, D.M.O. Chong b a Centre for Environmental Risk Management of Bushfires, Institute of Conservation Biology and Environmental Management, University of Wollongong, Wollongong, NSW 2522, Australia b School of Forest and Ecosystem Science, University of Melbourne, Burnley Campus, 500 Yarra Boulevard, Richmond, VIC 3121, Australia
a r t i c l e i n f o
a b s t r a c t
Article history: Received 18 March 2013 Received in revised form 19 August 2013 Accepted 10 October 2013 Available online 6 November 2013
Large budgets are spent on both suppression and fuel treatments in order to reduce the risk of wildfires. There is little evidence regarding the relative contribution of fire weather, suppression and fuel treatments in determining the risk posed from wildfires. Here we undertake a simulation study in the Sydney Basin, Australia, to examine this question using a fire behaviour model (Phoenix Rapidfire). Results of the study indicate that fire behaviour is most strongly influenced by fire weather. Suppression has a greater influence on whether a fire reaches 5 ha in size compared to fuel treatments. In contrast, fuel treatments have a stronger effect on the fire size and maximum distance the fire travels. The study suggests that fire management agencies will receive additional benefits from fuel treatment if they are located in areas which suppression resources can respond rapidly and attempt to contain the fires. No combination of treatments contained all fires, and the proportion of uncontained fires increased under more severe fire weather when the greatest number of properties are lost. Our study highlights the importance of alternative management strategies to reduce the risk of property loss. Crown Copyright Ó 2013 Published by Elsevier Ltd. All rights reserved.
Keywords: Prescribed burning Fire suppression Forest Fire behaviour
1. Introduction Wildfires result in significant economic and social costs when they encounter human assets (Cohen, 2000; Gill and Stephens, 2009; Lampin-Maillet et al., 2009). Wildfires in California, USA, in 2007 resulted in the evacuation of 300,000 people and the loss of 2223 houses (McCaffrey and Rhodes, 2009). The black Saturday fires in Victoria, Australia 2009 resulted in the loss of 173 lives, and more than 2000 houses (Leonard et al., 2009). In an attempt to reduce the number of losses, fire management agencies are spending increasing amounts on broad scale fuel treatment programs and active suppression of wildfires (Calkin et al., 2005). However, quantitative assessment of the optimal combination of these two management options is lacking. Suppression has the potential to reduce the size of any wildfire, and the resultant risk to people and property, if the rate of construction of the fire containment line greatly exceeds the rate of spread of the fire (Finney et al., 2009). Rate of containment line construction varies between the types of suppression resources used on the fire (e.g., Hirsch et al., 2004; Plucinski et al., 2011). For example, the effectiveness of aerial resources is limited by the
* Corresponding author. Tel.: þ61 2 4298 1232. E-mail address: [email protected] (T.D. Penman).
water holding capacity of the aircraft and the distance which it must travel to refill the water. In contrast, ground crews are limited by access to the fire, i.e. distance to road, number of staff and the water holding capacity of the vehicle. All resources are affected by environmental factors such as slope (McCarthy et al., 2012), vegetation type (Schmidt and Reinhardt, 1982), fuel loads (Plucinski, 2012) and fire intensity (Hirsch et al., 1998a). Similarly, the rate of spread and intensity of a fire increases with weather, fuel loads and slope (Noble et al., 1980; Collins et al., 2007; Thompson and Spies, 2009; Bradstock et al., 2010; Price and Bradstock, 2012). Suppression is therefore most likely to be successful in reducing risk under more benign fire weather conditions in lower fuel loads. Fuel reduction treatments are used globally as a preventative measure to reduce the risk of wildfires to people and property (see reviews by Fernandes and Botelho, 2003; Stephens et al., 2009; Penman et al., 2011a; Fulé et al., 2012). The practice is based on the fundamentals of fire behaviour arguing that a reduction in fuel loads will result in a subsequent lowering of the fire intensity and rate of spread (Noble et al., 1980; Bradstock et al., 2010; Price and Bradstock, 2010; Fulé et al., 2012). Prescribed burning (a broad area fuel treatment) in temperate forests has been found to reduce the extent of wildfires in the landscape (Boer et al., 2009; Price and Bradstock, 2011). The effectiveness of an individual treatment is relatively short lived (5 years) under severe fire weather (Bradstock
0301-4797/$ e see front matter Crown Copyright Ó 2013 Published by Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jenvman.2013.10.007
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T.D. Penman et al. / Journal of Environmental Management 131 (2013) 325e333
Fig. 1. Study area.
et al., 2010; Price and Bradstock, 2010) when the majority of area is burnt (Cunningham, 1984; Bradstock et al., 2009) and the greatest number of houses are lost (Blanchi et al., 2010). It is important to note that there is geographic variability in the effectiveness of prescribed burning, with some regions showing no effect (Price et al., 2012; Collins et al., 2013 #2379). Fuel reduction treatments are thought to aid suppression efforts (Fernandes and Botelho, 2003). If fuel treatments reduce the rate of spread of a fire, this will also decrease the size of the fire and hence the length of the fire line to be constructed. Furthermore, if fuel treatments decrease fire intensity the effectiveness of suppression is expected to increase. There is evidence to suggest that suppression resources are more likely to be effective where wildfires encountered fuels in a reduced state (Hirsch and Martell, 1996; Fernandes and Botelho, 2003; Plucinski et al., 2011). In a simulation study, Bradstock et al. (2012) found increasing fuel treatment rates resulted in modest reduction in the occurrence of fires beyond the limits of fire suppression, i.e. greater than 4000 kW/m. Although it should be noted that this study did not explicitly include suppression, and this may have increased the magnitude of the effect. There is a need to understand the extent to which fire suppression and fuel treatments can reduce the risk of wildfire to people and properties. The evidence suggests that the effectiveness of either of these treatments is dependent on the fire weather. In this paper, we undertake a fire simulation study in the topographically diverse landscape of the Sydney Basin bioregion. Specifically, we aim to determine what is the relative influence of fire weather, fire suppression and fuel treatment on fire behaviour. We focus on three variables e the probability of a fire reaching 5 ha, final fire size and the maximum distance a fire travels. 2. Materials and methods 2.1. Study area The Sydney Basin Bioregion, Australia (as defined by Environment Australia, 2000) contains three large urban centres
with a combined population of 5.5 million people (www.abs.gov.au, Accessed March 2012). Large tracts of fire prone native vegetation surround these centres. The simulation study was conducted using three such areas e Hornsby (105,000 ha), Woronora (179,000 ha) and the Blue Mountains (197,000 ha) (Fig. 1). These areas were used in a previous study which examined the interactive effects of prescribed burning strategies and climate change on wildfire characteristics and risk (Bradstock et al., 2012). All three areas are dissected sandstone/shale terrain managed predominantly for conservation or water yield. The vegetation in the study area is dominated by Eucalyptus forests with dry sclerophyll forests on the ridge and upper slopes and wet sclerophyll forests in the gullies (Keith, 2004). Smaller patches of rainforest and shrubby heath occur throughout the study area. The open dry sclerophyll forests dominate the area (ca 70%) and have dense shrub and graminoid understoreys (Bradstock et al., 2012). Litter accumulation is considered rapid with fine fuels (leaves and twigs < 25 mm) reaching pre fire levels within 5 years (Conroy, 1993) and heavy fuels taking approximately 15 years (Van Loon, 1977). 2.2. Simulation study We used the Phoenix Rapidfire model (hereafter Phoenix) (Tolhurst et al., 2008) to examine the interactive effects of suppression and fuel treatment under various weather scenarios. Phoenix simulates the two dimensional growth of fires in landscapes using Huygen’s principle (Knight and Coleman, 1993). Fire behaviour within Phoenix is based on a generalisation of the CSIRO southern grassland fire spread model (Cheney and Sullivan, 1997; Cheney et al., 1998) and a modified version of the McArthur Mk5 forest fire behaviour model (McArthur, 1967; Noble et al., 1980). The model incorporates a range of additional models including fuel accumulation as a function of time since fire, effects of topography and vegetation type on wind, based on the Wind Ninja program (http://www.firemodels.org/index.php/windninja-introduction e Accessed November 2011) and fire spotting (via ember propagation, spread and spot-fire ignition (Saeedian et al., 2010)). Fuel
T.D. Penman et al. / Journal of Environmental Management 131 (2013) 325e333
accumulation curves are based on the negative exponential curve (Olson, 1963) and were populated with data (Watson, 2011) for the major vegetation formations in the study area based on the classification of Keith (2004). Wet and dry sclerophyll forests in the study area exhibit rapid fuel accumulation rates so that surface fuels reach pre-fire levels within 5e10 years after fire (Conroy, 1993; Morrison et al., 1996; Penman and York, 2010). Breaks in fuels through streams and roads were incorporated from official records (NSW Govt unpub data, including estimates of their width). Fires could cross these breaks either through spotting or when flame length exceeded the width of the break. The official records were also used to determine proximity to road. A 30 m resolution digital elevation model was used to create the topographic inputs for the fire behaviour models. All other data were represented on a grid of 30 m resolution, but due to computational limitations, fire behaviour parameters such as rate of spread, intensity and ember density were estimated for 180 m grid cells as recommended by Tolhurst et al. (2008). 2.3. Fuel treatment To reproduce the patterns of fuel reduction in the study landscapes, we created a simulated fire history incorporating wildfires and prescribed fire treatments. Wildfire and prescribed fire histories were generated separately and then combined to form fire history datasets. Other forms of fuel treatment, such as mastication and fuel breaks (Potts and Stephens, 2009; Syphard et al., 2011), which are commonly applied in the USA were not considered in this study as they are not used in the Australian landscape. Four levels of prescribed burning effort were used in the study e zero, one, five and ten percent per annum. Each of the three study areas was divided into realistic treatment blocks in a previous study based on areas of native vegetation bounded by roads and/or drainage lines (Bradstock et al., 2012). Twenty prescribed fire histories were generated representing five replicates of each of the four levels of prescribed burning effort. Each treatment level was generated separately for each of the study areas for a thirty year period. To generate the prescribed fire history, the treatment blocks were randomly sampled until the treatment level was within 0.05% of the target burn level. A random selection was deemed appropriate as Bradstock et al. (2012) found random versus strategically targeted treatments in the general landscape had little effect on resultant wildfire size, intensity and impact on the urban interface. Wildfires in the region burn an average of 5% of the vegetated landscape per annum (Price and Bradstock, 2011). We modelled wildfires on the basis of temporal patterns of weather associated with recorded wildfires for the region using a ten year moving window. Wildfires were randomly selected from the fire history database until the threshold value was crossed, with the threshold being the average area burnt by wildfires in the previous 10 years adjusted for the reduction as a result of prescribed fire. Prescribed fire and wildfire histories were combined to develop 20 (four prescribed burning levels five replicates) fire history layers for each of the three areas. Layers were visually examined to ensure they represented realistic scenarios, i.e. they demonstrated realistic spatial and temporal spread of prescribed fires and wildfires. 2.4. Suppression A suppression module is included in the Phoenix Rapidfire program. The suppression module allows the user to define the time taken for resources to reach the fire after the ignition (i.e. the response time) and the number and type of resources and the relative weighting given to each flank. We considered three response times e one, two and 4 h after the ignition. These times
327
were selected as they reflect a combination of the delays in reporting ignitions and the difficulty in accessing some parts of the study area due to a lack of roads and the topography. In this study, we selected a simple representation of ground resources which calculates successful line construction rates based on topography (slope), distance from road, fuel load and fire intensity. When successful line construction occurs in Phoenix, the active fire front along the constructed line is extinguished. Three levels of line construction rates were selected (hereafter low, medium and high) representing uninhibited rates of line construction of one, two and 5 km per hour. All construction rates were for identical resources ensuring that the influence of topography, distance from road, vegetation type and fire intensity were equivocal. Resources were added to the flanks in a 60:40 ratio, favouring the leeward side of the fire. 2.5. Fire weather The McArthur Forest Fire Danger Index (FFDI) is a measure of fire weather and potential fire behaviour (Noble et al., 1980). It is based on a combination of temperature, humidity, rainfall, average wind speed and longer term drying through a drought factor (Noble et al., 1980). Hourly FFDI values were estimated for a period of 40 years at Australian Bureau of Meteorology weather stations within the Sydney Basin with appropriate data e namely Richmond, Sydney Airport, Mt Boyce, Cessnock, Moss Vale and Katoomba. Daily maximum FFDI on days in which fires occurred was classified into five categories e Low 0e12, High 13e25, Very High 26e50, Severe 51e75 and Extreme 76e100. From the resultant sample (n ¼ 863), five replicate days were drawn at random from each category for use in simulations resulting in 25 weather streams. Fires were restricted to a single day to minimise permutations. Most area burned by individual fires in the region occurs in a single day (Cunningham, 1984; Bradstock et al., 2009). All simulated ignitions in the three study areas were exposed to the same 25 weather streams. 2.6. Fires One hundred random ignition locations were generated within each study area using GRASS GIS v6.5 (GRASS Development Team, 2010). While there are spatial patterns in ignitions within the study area (Penman et al., 2013), predictions based on these models suggest that ignitions could occur throughout the study area and for this reason random locations were used. All ignitions were allowed to run for every combination of weather, fuel treatment and suppression. This resulted in a total of 1,500,000 simulated fires (300 ignitions 25 weather streams 20 fire history [3 response times 3 suppression levels þ 1 control, i.e. no suppression]). The weather stream started at 0000 h and fires were ignited at 1000 h to allow the program to generate stable and realistic estimates of fuel moisture. Once ignited, the fires were allowed to run up until 2200 (12 h). 2.7. Statistical analysis To examine for interactive effects of weather, fuel treatment and suppression we used three response variables e probability of a fire that does not exceed 5 ha, final fire area and maximum distance travelled from the ignition point in the 12 h simulation. These variables are representations of the risk posed by the fire. Point based metrics such as fire intensity or flame length influence the success of suppression, hence probability of reaching 5ha fire size and distance travelled, and are therefore not explicitly considered. Models were generated in a generalised linear model framework
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using multiple model inference (Burnham and Anderson, 2002). The probability of fire being contained to less than 5 ha was modelled using a binomial response. These models were used to simulate an initial attack scenario. Models of fire size and distance travelled excluded fires that did not reach 5 ha to avoid overlap with the model of containment of fires at 5 ha. A Gaussian distribution was used in these models with the response variable being transformed with a natural logarithm to meet the assumption of normality. To avoid complexities associated with analysing and interpreting four way interactions, data were broken up into three response times (i.e. 1 hr, 2 h, 4 h) and analysis conducted for each subset. Due to the large size of the datasets, a random subset of 10,000 data points was taken for each of the modelling processes. Models representing all additive and interactive combinations of weather, fuel treatment and suppression were considered, as well as a null model. Only a single interaction term was included in each model for ease of interpretation. Comparisons between models were made using Akaike Information Criterion (AIC) (Akaike, 1973) and AIC weights (Burnham and Anderson, 2002). Using AIC model selection, models within 2 AIC points of the best model are considered to have strong support, those within 7 AIC have some support and those greater than 10 AIC points from the best model have no support (Burnham and Anderson, 2002). Hierarchical partitioning was used as an exploratory tool to examine the relative importance of fire weather (FFDI), fuel treatment and suppression in models predicting each of the three response variables. The method uses goodness of fit measures for all possible models with the predictor variables. The goodness of fit measures is partitioned to estimate the total independent contribution of each of the predictor variables (Chevan and Sutherland, 1991; MacNally, 2002). This method does not allow for interaction terms, therefore we only consider the additive combination of the primary terms.
3. Results 3.1. Probability of fire reaching 5 ha The best model predicting the probability of containing fires at less than 5 ha for one and 2 h response times was an additive effect of prescribed burning effort and an interaction between FFDI and suppression effort (Table 1). These models had an AIC weight of 1.000 and no other models fell within 10 AIC points. For response times of 4 h, the best model included FFDI plus an interaction between prescribed burning effort and suppression effort (AIC weight ¼ 0.915). A model with the additive effects of prescribed burning effort, suppression effort and FFDI was 4.9 AIC points higher than the best model and had an AIC weight of 0.078, indicating some support (Table 1). As this model is a simplified form of the best model and has a low AIC weight, we focus solely on the
Table 1 AIC model outputs for the models predicting the probability of the fire reaching 5 ha. Only models within 10 AIC points of the best model are shown. Model 1 h response p(5 ha) w FFDI* SUP þ PB 2 h response p(5 ha) w FFDI* SUP þ PB 4 h response p(5 ha) w FFDI þ PB* SUP p(5 ha) w FFDI þ PB þ SUP
AIC
Delta AIC
Model weight
12474.69
0
1.000
12622.95 AIC 12608.6 12613.5
0 Delta AIC 0 4.92
1.000 0.915 0.078
FFDI ¼ Forest Fire Danger Index; PB ¼ Prescribed burning treatment; SUP ¼ Suppression effort. NB model weights calculated on the entire model set.
best model. The deviance explained for the best model was 11, 9 and 9% for response times of 1, 2 and 4 h. Across all response times the effect of FFDI was greater than suppression or prescribed burning (Fig. 2). Furthermore, FFDI provided the greatest contribution to the model fit (Fig. 3). Probability of containing a fire to 5 ha was significantly higher in the 10% prescribed burning treatment, compared with the 0, 1 and 5% treatment levels (p < 0.001). However, across all levels of FFDI and suppression increasing prescribed burning treatment from 0% to 10% resulted in an approximately 0.1 increase in probability of containing the fire to 5 ha. No significant differences were recorded between the other treatments. Suppression effort increased the probability of containment. The only significant difference was between the high and low suppression efforts for all response times (p < 0.001), where the probability of containment for the high suppression scenarios was a maximum of 0.1 higher than the low efforts (Fig. 2). While interactions were included in all of the models for all response times, post-hoc comparisons indicate that the magnitude of these differences were very small in comparison to the main effects of FFDI, prescribed burning and suppression effort (Fig. 2). 3.2. Fire size Of the remaining fires (i.e. >5 ha), fire size ranged from 5 to approximately 49,000 ha, with a mean (standard deviation) of 1827 3688 ha. The best model for fire size included an additive effect of suppression (p < 0.001) and an interactive effect of fire weather and fuel treatment (p < 0.001). No other models were within 10 AIC points for response times of 1, 2 or 4 h (Table 2) indicating that alternative models had no support (Burnham and Anderson, 2002). The deviance explained by the best model was 19, 24, and 27% for response times of 1, 2 and 4 h respectively. FFDI had the greatest influence on the fire size reached (Fig. 4) and provided the greatest contribution to model fit (Fig. 3). Increasing FFDI resulted in a significant increase in the mean fire size (p < 0.001). In contrast, the effects of suppression and prescribed burning were relatively small (Fig. 4). Regardless, increasing suppression effort and decreasing response time resulted in a decrease in predicted mean fire size, with significant differences between all comparisons (p < 0.001). All fuel treatment levels resulted in a reduction in fire size, with the effect increasing with effort (p < 0.001). The effect of fuel treatment was more pronounced at higher values of FFDI (Fig. 4). 3.3. Maximum distance travelled Maximum distance travelled in the 12 h simulation ranged from 12 m to 46.6 km, with a mean (standard deviation) of 1.5 3.3 km. Patterns for distance travelled were consistent with those observed for fire size with the best model including an additive effect of suppression effort (p < 0.001) and an interactive effect of fire weather and fuel treatment (p < 0.001). As with the models for fire size, no other models were within 10 AIC points for response times of 1, 2 or 4 h (Table 3) indicating that alternative models had no support (Burnham and Anderson, 2002). The deviance explained by the best model was 20, 22, and 25% for response times of 1, 2 and 4 h respectively. FFDI had the greatest influence on the maximum distance travelled (Fig. 5) and provided the greatest contribution to the model fit (Fig. 3). Increasing suppression effort resulted in a decrease in predicted maximum distance travelled, with significant differences between all treatment levels (p < 0.001), however the magnitude of the differences were small (Fig. 5). Increasing fuel treatment levels resulted in a reduction in maximum distance
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Fig. 2. Predicted probability of containing fires less than 5 ha as a function of suppression effort, fuel treatment and weather. Predictions are made for the best model. Y-axis represents the probability of containing fires less than 5 ha, X-axis represents the fire weather. Line types represent the varying fuel treatments e solid line 0% per annum, long dash 1% per annum, dotted line 5% per annum, dash and dot line 10% per annum.
Fig. 3. Relative contribution of the main effects based on the analysis using hierarchical partitioning. Black represents the 1 h response time model, dark grey the 2 h response time model and the light grey the 4 h response time model.
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T.D. Penman et al. / Journal of Environmental Management 131 (2013) 325e333
Table 2 AIC model outputs for the models predicting the fire size. Only models within 10 AIC points of the best model are shown. Model 1 h response log(Fire size) w FFDI* PB þ SUP 2 h response log(Fire size) w FFDI* PB þ SUP 4 h response log(Fire size) w FFDI* PB þ SUP
AIC
Delta AIC
Model weight
45420.89
0
1.000
44108.8
0
1.000
41764.08
0
1.000
FFDI ¼ Forest Fire Danger Index; PB ¼ Prescribed burning treatment; SUP ¼ Suppression effort.
travelled for comparisons between all treatments (p < 0.001), except the 0 and 1% treatment levels (p ¼ 0.41). The effect of fuel treatment became larger at high values of FFDI (Fig. 4). 4. Discussion Individual effects of weather, fuel treatment and suppression observed in the study were consistent with previous empirical and simulation studies. Fire weather had the strongest effect on the probability of containment, fire size and distance travelled (Bessie and Johnson, 1995; Arienti et al., 2006; Archibald et al., 2009; Cary et al., 2009; Bradstock et al., 2010). Increasing suppression effort and decreasing response time decreased average fire size and distance travelled by a wildfire (Islam and Martell, 1998; Wilson and Wiitala, 2005; Plucinski, 2012). Increasing levels of fuel treatment resulted in smaller wildfires and distance travelled (Finney et al., 2007; Boer et al., 2009; Price and Bradstock, 2011).
Table 3 AIC model outputs for the models predicting the maximum distance travelled. Only models within 10 AIC points of the best model, i.e. the best set, are shown. Model 1 h response log(Distance) w FFDI* PB þ SUP 2 h response log(Distance) w FFDI* PB þ SUP 4 h response log(Distance) w FFDI* PB þ SUP
AIC
Delta AIC
Model weight
33694.83
0
0.996
33752.83
0
1.000
32251.54
0
1.000
FFDI ¼ Forest Fire Danger Index; PB ¼ Prescribed burning treatment; SUP ¼ Suppression effort.
Consistency between the patterns recorded in published empirical analyses with our simulation study provides support for the results of the simulation study. Unsurprisingly, fire weather had the strongest influence on the fire behaviour parameters independent of the management treatments (Figs. 2e5). Fire weather strongly influences the rate of spread, spotting distance, fire intensity and severity (Noble et al., 1980; Collins et al., 2007; Thompson and Spies, 2009; Bradstock et al., 2010; Price and Bradstock, 2012). As a result, the vast majority of the annual area burnt is from wildfires burning under these extreme conditions (Bradstock et al., 2009; Cary et al., 2009). It is also under these conditions that wildfires pose the greatest threat to people and property (Bradstock et al., 1998; Blanchi et al., 2010). While management may be effective under more benign fire weather scenarios, understanding the effectiveness of management under more extreme fire weather will be fundamental to understanding how management can change risk to people and property.
Fig. 4. Predicted mean fire size in hectares for fires greater than 5 ha as a function of suppression effort, fuel treatment and weather. Predictions are made for the best model. Y-axis represents mean fire size on a log scale, X-axis represents the fire weather. Line types represent the varying fuel treatments e solid line 0% per annum, long dash 1% per annum, dotted line 5% per annum, dash and dot line 10% per annum.
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Fig. 5. Predicted mean maximum distance travelled in metres as a function of suppression effort, fuel treatment and weather. Predictions are made for the best model. Y-axis represents mean maximum distance travelled on a log scale, X-axis represents the fire weather. Line types represent the varying fuel treatments e solid line 0% per annum, long dash 1% per annum, dotted line 5% per annum, dash and dot line 10% per annum.
At extreme FFDI values increased suppression effort and decreased response time resulted in the greatest effect on the probability of containment within 5 ha, whereas fuel treatment had a stronger influence on fire size and distance travelled (Figs. 2, 4 and 5). When fires are small (
Contents lists available at ScienceDirect
Journal of Environmental Management journal homepage: www.elsevier.com/locate/jenvman
Examining the relative effects of fire weather, suppression and fuel treatment on fire behaviour e A simulation study T.D. Penman a, *, L. Collins a, O.F. Price a, R.A. Bradstock a, S. Metcalf a, D.M.O. Chong b a Centre for Environmental Risk Management of Bushfires, Institute of Conservation Biology and Environmental Management, University of Wollongong, Wollongong, NSW 2522, Australia b School of Forest and Ecosystem Science, University of Melbourne, Burnley Campus, 500 Yarra Boulevard, Richmond, VIC 3121, Australia
a r t i c l e i n f o
a b s t r a c t
Article history: Received 18 March 2013 Received in revised form 19 August 2013 Accepted 10 October 2013 Available online 6 November 2013
Large budgets are spent on both suppression and fuel treatments in order to reduce the risk of wildfires. There is little evidence regarding the relative contribution of fire weather, suppression and fuel treatments in determining the risk posed from wildfires. Here we undertake a simulation study in the Sydney Basin, Australia, to examine this question using a fire behaviour model (Phoenix Rapidfire). Results of the study indicate that fire behaviour is most strongly influenced by fire weather. Suppression has a greater influence on whether a fire reaches 5 ha in size compared to fuel treatments. In contrast, fuel treatments have a stronger effect on the fire size and maximum distance the fire travels. The study suggests that fire management agencies will receive additional benefits from fuel treatment if they are located in areas which suppression resources can respond rapidly and attempt to contain the fires. No combination of treatments contained all fires, and the proportion of uncontained fires increased under more severe fire weather when the greatest number of properties are lost. Our study highlights the importance of alternative management strategies to reduce the risk of property loss. Crown Copyright Ó 2013 Published by Elsevier Ltd. All rights reserved.
Keywords: Prescribed burning Fire suppression Forest Fire behaviour
1. Introduction Wildfires result in significant economic and social costs when they encounter human assets (Cohen, 2000; Gill and Stephens, 2009; Lampin-Maillet et al., 2009). Wildfires in California, USA, in 2007 resulted in the evacuation of 300,000 people and the loss of 2223 houses (McCaffrey and Rhodes, 2009). The black Saturday fires in Victoria, Australia 2009 resulted in the loss of 173 lives, and more than 2000 houses (Leonard et al., 2009). In an attempt to reduce the number of losses, fire management agencies are spending increasing amounts on broad scale fuel treatment programs and active suppression of wildfires (Calkin et al., 2005). However, quantitative assessment of the optimal combination of these two management options is lacking. Suppression has the potential to reduce the size of any wildfire, and the resultant risk to people and property, if the rate of construction of the fire containment line greatly exceeds the rate of spread of the fire (Finney et al., 2009). Rate of containment line construction varies between the types of suppression resources used on the fire (e.g., Hirsch et al., 2004; Plucinski et al., 2011). For example, the effectiveness of aerial resources is limited by the
* Corresponding author. Tel.: þ61 2 4298 1232. E-mail address: [email protected] (T.D. Penman).
water holding capacity of the aircraft and the distance which it must travel to refill the water. In contrast, ground crews are limited by access to the fire, i.e. distance to road, number of staff and the water holding capacity of the vehicle. All resources are affected by environmental factors such as slope (McCarthy et al., 2012), vegetation type (Schmidt and Reinhardt, 1982), fuel loads (Plucinski, 2012) and fire intensity (Hirsch et al., 1998a). Similarly, the rate of spread and intensity of a fire increases with weather, fuel loads and slope (Noble et al., 1980; Collins et al., 2007; Thompson and Spies, 2009; Bradstock et al., 2010; Price and Bradstock, 2012). Suppression is therefore most likely to be successful in reducing risk under more benign fire weather conditions in lower fuel loads. Fuel reduction treatments are used globally as a preventative measure to reduce the risk of wildfires to people and property (see reviews by Fernandes and Botelho, 2003; Stephens et al., 2009; Penman et al., 2011a; Fulé et al., 2012). The practice is based on the fundamentals of fire behaviour arguing that a reduction in fuel loads will result in a subsequent lowering of the fire intensity and rate of spread (Noble et al., 1980; Bradstock et al., 2010; Price and Bradstock, 2010; Fulé et al., 2012). Prescribed burning (a broad area fuel treatment) in temperate forests has been found to reduce the extent of wildfires in the landscape (Boer et al., 2009; Price and Bradstock, 2011). The effectiveness of an individual treatment is relatively short lived (5 years) under severe fire weather (Bradstock
0301-4797/$ e see front matter Crown Copyright Ó 2013 Published by Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jenvman.2013.10.007
326
T.D. Penman et al. / Journal of Environmental Management 131 (2013) 325e333
Fig. 1. Study area.
et al., 2010; Price and Bradstock, 2010) when the majority of area is burnt (Cunningham, 1984; Bradstock et al., 2009) and the greatest number of houses are lost (Blanchi et al., 2010). It is important to note that there is geographic variability in the effectiveness of prescribed burning, with some regions showing no effect (Price et al., 2012; Collins et al., 2013 #2379). Fuel reduction treatments are thought to aid suppression efforts (Fernandes and Botelho, 2003). If fuel treatments reduce the rate of spread of a fire, this will also decrease the size of the fire and hence the length of the fire line to be constructed. Furthermore, if fuel treatments decrease fire intensity the effectiveness of suppression is expected to increase. There is evidence to suggest that suppression resources are more likely to be effective where wildfires encountered fuels in a reduced state (Hirsch and Martell, 1996; Fernandes and Botelho, 2003; Plucinski et al., 2011). In a simulation study, Bradstock et al. (2012) found increasing fuel treatment rates resulted in modest reduction in the occurrence of fires beyond the limits of fire suppression, i.e. greater than 4000 kW/m. Although it should be noted that this study did not explicitly include suppression, and this may have increased the magnitude of the effect. There is a need to understand the extent to which fire suppression and fuel treatments can reduce the risk of wildfire to people and properties. The evidence suggests that the effectiveness of either of these treatments is dependent on the fire weather. In this paper, we undertake a fire simulation study in the topographically diverse landscape of the Sydney Basin bioregion. Specifically, we aim to determine what is the relative influence of fire weather, fire suppression and fuel treatment on fire behaviour. We focus on three variables e the probability of a fire reaching 5 ha, final fire size and the maximum distance a fire travels. 2. Materials and methods 2.1. Study area The Sydney Basin Bioregion, Australia (as defined by Environment Australia, 2000) contains three large urban centres
with a combined population of 5.5 million people (www.abs.gov.au, Accessed March 2012). Large tracts of fire prone native vegetation surround these centres. The simulation study was conducted using three such areas e Hornsby (105,000 ha), Woronora (179,000 ha) and the Blue Mountains (197,000 ha) (Fig. 1). These areas were used in a previous study which examined the interactive effects of prescribed burning strategies and climate change on wildfire characteristics and risk (Bradstock et al., 2012). All three areas are dissected sandstone/shale terrain managed predominantly for conservation or water yield. The vegetation in the study area is dominated by Eucalyptus forests with dry sclerophyll forests on the ridge and upper slopes and wet sclerophyll forests in the gullies (Keith, 2004). Smaller patches of rainforest and shrubby heath occur throughout the study area. The open dry sclerophyll forests dominate the area (ca 70%) and have dense shrub and graminoid understoreys (Bradstock et al., 2012). Litter accumulation is considered rapid with fine fuels (leaves and twigs < 25 mm) reaching pre fire levels within 5 years (Conroy, 1993) and heavy fuels taking approximately 15 years (Van Loon, 1977). 2.2. Simulation study We used the Phoenix Rapidfire model (hereafter Phoenix) (Tolhurst et al., 2008) to examine the interactive effects of suppression and fuel treatment under various weather scenarios. Phoenix simulates the two dimensional growth of fires in landscapes using Huygen’s principle (Knight and Coleman, 1993). Fire behaviour within Phoenix is based on a generalisation of the CSIRO southern grassland fire spread model (Cheney and Sullivan, 1997; Cheney et al., 1998) and a modified version of the McArthur Mk5 forest fire behaviour model (McArthur, 1967; Noble et al., 1980). The model incorporates a range of additional models including fuel accumulation as a function of time since fire, effects of topography and vegetation type on wind, based on the Wind Ninja program (http://www.firemodels.org/index.php/windninja-introduction e Accessed November 2011) and fire spotting (via ember propagation, spread and spot-fire ignition (Saeedian et al., 2010)). Fuel
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accumulation curves are based on the negative exponential curve (Olson, 1963) and were populated with data (Watson, 2011) for the major vegetation formations in the study area based on the classification of Keith (2004). Wet and dry sclerophyll forests in the study area exhibit rapid fuel accumulation rates so that surface fuels reach pre-fire levels within 5e10 years after fire (Conroy, 1993; Morrison et al., 1996; Penman and York, 2010). Breaks in fuels through streams and roads were incorporated from official records (NSW Govt unpub data, including estimates of their width). Fires could cross these breaks either through spotting or when flame length exceeded the width of the break. The official records were also used to determine proximity to road. A 30 m resolution digital elevation model was used to create the topographic inputs for the fire behaviour models. All other data were represented on a grid of 30 m resolution, but due to computational limitations, fire behaviour parameters such as rate of spread, intensity and ember density were estimated for 180 m grid cells as recommended by Tolhurst et al. (2008). 2.3. Fuel treatment To reproduce the patterns of fuel reduction in the study landscapes, we created a simulated fire history incorporating wildfires and prescribed fire treatments. Wildfire and prescribed fire histories were generated separately and then combined to form fire history datasets. Other forms of fuel treatment, such as mastication and fuel breaks (Potts and Stephens, 2009; Syphard et al., 2011), which are commonly applied in the USA were not considered in this study as they are not used in the Australian landscape. Four levels of prescribed burning effort were used in the study e zero, one, five and ten percent per annum. Each of the three study areas was divided into realistic treatment blocks in a previous study based on areas of native vegetation bounded by roads and/or drainage lines (Bradstock et al., 2012). Twenty prescribed fire histories were generated representing five replicates of each of the four levels of prescribed burning effort. Each treatment level was generated separately for each of the study areas for a thirty year period. To generate the prescribed fire history, the treatment blocks were randomly sampled until the treatment level was within 0.05% of the target burn level. A random selection was deemed appropriate as Bradstock et al. (2012) found random versus strategically targeted treatments in the general landscape had little effect on resultant wildfire size, intensity and impact on the urban interface. Wildfires in the region burn an average of 5% of the vegetated landscape per annum (Price and Bradstock, 2011). We modelled wildfires on the basis of temporal patterns of weather associated with recorded wildfires for the region using a ten year moving window. Wildfires were randomly selected from the fire history database until the threshold value was crossed, with the threshold being the average area burnt by wildfires in the previous 10 years adjusted for the reduction as a result of prescribed fire. Prescribed fire and wildfire histories were combined to develop 20 (four prescribed burning levels five replicates) fire history layers for each of the three areas. Layers were visually examined to ensure they represented realistic scenarios, i.e. they demonstrated realistic spatial and temporal spread of prescribed fires and wildfires. 2.4. Suppression A suppression module is included in the Phoenix Rapidfire program. The suppression module allows the user to define the time taken for resources to reach the fire after the ignition (i.e. the response time) and the number and type of resources and the relative weighting given to each flank. We considered three response times e one, two and 4 h after the ignition. These times
327
were selected as they reflect a combination of the delays in reporting ignitions and the difficulty in accessing some parts of the study area due to a lack of roads and the topography. In this study, we selected a simple representation of ground resources which calculates successful line construction rates based on topography (slope), distance from road, fuel load and fire intensity. When successful line construction occurs in Phoenix, the active fire front along the constructed line is extinguished. Three levels of line construction rates were selected (hereafter low, medium and high) representing uninhibited rates of line construction of one, two and 5 km per hour. All construction rates were for identical resources ensuring that the influence of topography, distance from road, vegetation type and fire intensity were equivocal. Resources were added to the flanks in a 60:40 ratio, favouring the leeward side of the fire. 2.5. Fire weather The McArthur Forest Fire Danger Index (FFDI) is a measure of fire weather and potential fire behaviour (Noble et al., 1980). It is based on a combination of temperature, humidity, rainfall, average wind speed and longer term drying through a drought factor (Noble et al., 1980). Hourly FFDI values were estimated for a period of 40 years at Australian Bureau of Meteorology weather stations within the Sydney Basin with appropriate data e namely Richmond, Sydney Airport, Mt Boyce, Cessnock, Moss Vale and Katoomba. Daily maximum FFDI on days in which fires occurred was classified into five categories e Low 0e12, High 13e25, Very High 26e50, Severe 51e75 and Extreme 76e100. From the resultant sample (n ¼ 863), five replicate days were drawn at random from each category for use in simulations resulting in 25 weather streams. Fires were restricted to a single day to minimise permutations. Most area burned by individual fires in the region occurs in a single day (Cunningham, 1984; Bradstock et al., 2009). All simulated ignitions in the three study areas were exposed to the same 25 weather streams. 2.6. Fires One hundred random ignition locations were generated within each study area using GRASS GIS v6.5 (GRASS Development Team, 2010). While there are spatial patterns in ignitions within the study area (Penman et al., 2013), predictions based on these models suggest that ignitions could occur throughout the study area and for this reason random locations were used. All ignitions were allowed to run for every combination of weather, fuel treatment and suppression. This resulted in a total of 1,500,000 simulated fires (300 ignitions 25 weather streams 20 fire history [3 response times 3 suppression levels þ 1 control, i.e. no suppression]). The weather stream started at 0000 h and fires were ignited at 1000 h to allow the program to generate stable and realistic estimates of fuel moisture. Once ignited, the fires were allowed to run up until 2200 (12 h). 2.7. Statistical analysis To examine for interactive effects of weather, fuel treatment and suppression we used three response variables e probability of a fire that does not exceed 5 ha, final fire area and maximum distance travelled from the ignition point in the 12 h simulation. These variables are representations of the risk posed by the fire. Point based metrics such as fire intensity or flame length influence the success of suppression, hence probability of reaching 5ha fire size and distance travelled, and are therefore not explicitly considered. Models were generated in a generalised linear model framework
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using multiple model inference (Burnham and Anderson, 2002). The probability of fire being contained to less than 5 ha was modelled using a binomial response. These models were used to simulate an initial attack scenario. Models of fire size and distance travelled excluded fires that did not reach 5 ha to avoid overlap with the model of containment of fires at 5 ha. A Gaussian distribution was used in these models with the response variable being transformed with a natural logarithm to meet the assumption of normality. To avoid complexities associated with analysing and interpreting four way interactions, data were broken up into three response times (i.e. 1 hr, 2 h, 4 h) and analysis conducted for each subset. Due to the large size of the datasets, a random subset of 10,000 data points was taken for each of the modelling processes. Models representing all additive and interactive combinations of weather, fuel treatment and suppression were considered, as well as a null model. Only a single interaction term was included in each model for ease of interpretation. Comparisons between models were made using Akaike Information Criterion (AIC) (Akaike, 1973) and AIC weights (Burnham and Anderson, 2002). Using AIC model selection, models within 2 AIC points of the best model are considered to have strong support, those within 7 AIC have some support and those greater than 10 AIC points from the best model have no support (Burnham and Anderson, 2002). Hierarchical partitioning was used as an exploratory tool to examine the relative importance of fire weather (FFDI), fuel treatment and suppression in models predicting each of the three response variables. The method uses goodness of fit measures for all possible models with the predictor variables. The goodness of fit measures is partitioned to estimate the total independent contribution of each of the predictor variables (Chevan and Sutherland, 1991; MacNally, 2002). This method does not allow for interaction terms, therefore we only consider the additive combination of the primary terms.
3. Results 3.1. Probability of fire reaching 5 ha The best model predicting the probability of containing fires at less than 5 ha for one and 2 h response times was an additive effect of prescribed burning effort and an interaction between FFDI and suppression effort (Table 1). These models had an AIC weight of 1.000 and no other models fell within 10 AIC points. For response times of 4 h, the best model included FFDI plus an interaction between prescribed burning effort and suppression effort (AIC weight ¼ 0.915). A model with the additive effects of prescribed burning effort, suppression effort and FFDI was 4.9 AIC points higher than the best model and had an AIC weight of 0.078, indicating some support (Table 1). As this model is a simplified form of the best model and has a low AIC weight, we focus solely on the
Table 1 AIC model outputs for the models predicting the probability of the fire reaching 5 ha. Only models within 10 AIC points of the best model are shown. Model 1 h response p(5 ha) w FFDI* SUP þ PB 2 h response p(5 ha) w FFDI* SUP þ PB 4 h response p(5 ha) w FFDI þ PB* SUP p(5 ha) w FFDI þ PB þ SUP
AIC
Delta AIC
Model weight
12474.69
0
1.000
12622.95 AIC 12608.6 12613.5
0 Delta AIC 0 4.92
1.000 0.915 0.078
FFDI ¼ Forest Fire Danger Index; PB ¼ Prescribed burning treatment; SUP ¼ Suppression effort. NB model weights calculated on the entire model set.
best model. The deviance explained for the best model was 11, 9 and 9% for response times of 1, 2 and 4 h. Across all response times the effect of FFDI was greater than suppression or prescribed burning (Fig. 2). Furthermore, FFDI provided the greatest contribution to the model fit (Fig. 3). Probability of containing a fire to 5 ha was significantly higher in the 10% prescribed burning treatment, compared with the 0, 1 and 5% treatment levels (p < 0.001). However, across all levels of FFDI and suppression increasing prescribed burning treatment from 0% to 10% resulted in an approximately 0.1 increase in probability of containing the fire to 5 ha. No significant differences were recorded between the other treatments. Suppression effort increased the probability of containment. The only significant difference was between the high and low suppression efforts for all response times (p < 0.001), where the probability of containment for the high suppression scenarios was a maximum of 0.1 higher than the low efforts (Fig. 2). While interactions were included in all of the models for all response times, post-hoc comparisons indicate that the magnitude of these differences were very small in comparison to the main effects of FFDI, prescribed burning and suppression effort (Fig. 2). 3.2. Fire size Of the remaining fires (i.e. >5 ha), fire size ranged from 5 to approximately 49,000 ha, with a mean (standard deviation) of 1827 3688 ha. The best model for fire size included an additive effect of suppression (p < 0.001) and an interactive effect of fire weather and fuel treatment (p < 0.001). No other models were within 10 AIC points for response times of 1, 2 or 4 h (Table 2) indicating that alternative models had no support (Burnham and Anderson, 2002). The deviance explained by the best model was 19, 24, and 27% for response times of 1, 2 and 4 h respectively. FFDI had the greatest influence on the fire size reached (Fig. 4) and provided the greatest contribution to model fit (Fig. 3). Increasing FFDI resulted in a significant increase in the mean fire size (p < 0.001). In contrast, the effects of suppression and prescribed burning were relatively small (Fig. 4). Regardless, increasing suppression effort and decreasing response time resulted in a decrease in predicted mean fire size, with significant differences between all comparisons (p < 0.001). All fuel treatment levels resulted in a reduction in fire size, with the effect increasing with effort (p < 0.001). The effect of fuel treatment was more pronounced at higher values of FFDI (Fig. 4). 3.3. Maximum distance travelled Maximum distance travelled in the 12 h simulation ranged from 12 m to 46.6 km, with a mean (standard deviation) of 1.5 3.3 km. Patterns for distance travelled were consistent with those observed for fire size with the best model including an additive effect of suppression effort (p < 0.001) and an interactive effect of fire weather and fuel treatment (p < 0.001). As with the models for fire size, no other models were within 10 AIC points for response times of 1, 2 or 4 h (Table 3) indicating that alternative models had no support (Burnham and Anderson, 2002). The deviance explained by the best model was 20, 22, and 25% for response times of 1, 2 and 4 h respectively. FFDI had the greatest influence on the maximum distance travelled (Fig. 5) and provided the greatest contribution to the model fit (Fig. 3). Increasing suppression effort resulted in a decrease in predicted maximum distance travelled, with significant differences between all treatment levels (p < 0.001), however the magnitude of the differences were small (Fig. 5). Increasing fuel treatment levels resulted in a reduction in maximum distance
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Fig. 2. Predicted probability of containing fires less than 5 ha as a function of suppression effort, fuel treatment and weather. Predictions are made for the best model. Y-axis represents the probability of containing fires less than 5 ha, X-axis represents the fire weather. Line types represent the varying fuel treatments e solid line 0% per annum, long dash 1% per annum, dotted line 5% per annum, dash and dot line 10% per annum.
Fig. 3. Relative contribution of the main effects based on the analysis using hierarchical partitioning. Black represents the 1 h response time model, dark grey the 2 h response time model and the light grey the 4 h response time model.
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Table 2 AIC model outputs for the models predicting the fire size. Only models within 10 AIC points of the best model are shown. Model 1 h response log(Fire size) w FFDI* PB þ SUP 2 h response log(Fire size) w FFDI* PB þ SUP 4 h response log(Fire size) w FFDI* PB þ SUP
AIC
Delta AIC
Model weight
45420.89
0
1.000
44108.8
0
1.000
41764.08
0
1.000
FFDI ¼ Forest Fire Danger Index; PB ¼ Prescribed burning treatment; SUP ¼ Suppression effort.
travelled for comparisons between all treatments (p < 0.001), except the 0 and 1% treatment levels (p ¼ 0.41). The effect of fuel treatment became larger at high values of FFDI (Fig. 4). 4. Discussion Individual effects of weather, fuel treatment and suppression observed in the study were consistent with previous empirical and simulation studies. Fire weather had the strongest effect on the probability of containment, fire size and distance travelled (Bessie and Johnson, 1995; Arienti et al., 2006; Archibald et al., 2009; Cary et al., 2009; Bradstock et al., 2010). Increasing suppression effort and decreasing response time decreased average fire size and distance travelled by a wildfire (Islam and Martell, 1998; Wilson and Wiitala, 2005; Plucinski, 2012). Increasing levels of fuel treatment resulted in smaller wildfires and distance travelled (Finney et al., 2007; Boer et al., 2009; Price and Bradstock, 2011).
Table 3 AIC model outputs for the models predicting the maximum distance travelled. Only models within 10 AIC points of the best model, i.e. the best set, are shown. Model 1 h response log(Distance) w FFDI* PB þ SUP 2 h response log(Distance) w FFDI* PB þ SUP 4 h response log(Distance) w FFDI* PB þ SUP
AIC
Delta AIC
Model weight
33694.83
0
0.996
33752.83
0
1.000
32251.54
0
1.000
FFDI ¼ Forest Fire Danger Index; PB ¼ Prescribed burning treatment; SUP ¼ Suppression effort.
Consistency between the patterns recorded in published empirical analyses with our simulation study provides support for the results of the simulation study. Unsurprisingly, fire weather had the strongest influence on the fire behaviour parameters independent of the management treatments (Figs. 2e5). Fire weather strongly influences the rate of spread, spotting distance, fire intensity and severity (Noble et al., 1980; Collins et al., 2007; Thompson and Spies, 2009; Bradstock et al., 2010; Price and Bradstock, 2012). As a result, the vast majority of the annual area burnt is from wildfires burning under these extreme conditions (Bradstock et al., 2009; Cary et al., 2009). It is also under these conditions that wildfires pose the greatest threat to people and property (Bradstock et al., 1998; Blanchi et al., 2010). While management may be effective under more benign fire weather scenarios, understanding the effectiveness of management under more extreme fire weather will be fundamental to understanding how management can change risk to people and property.
Fig. 4. Predicted mean fire size in hectares for fires greater than 5 ha as a function of suppression effort, fuel treatment and weather. Predictions are made for the best model. Y-axis represents mean fire size on a log scale, X-axis represents the fire weather. Line types represent the varying fuel treatments e solid line 0% per annum, long dash 1% per annum, dotted line 5% per annum, dash and dot line 10% per annum.
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Fig. 5. Predicted mean maximum distance travelled in metres as a function of suppression effort, fuel treatment and weather. Predictions are made for the best model. Y-axis represents mean maximum distance travelled on a log scale, X-axis represents the fire weather. Line types represent the varying fuel treatments e solid line 0% per annum, long dash 1% per annum, dotted line 5% per annum, dash and dot line 10% per annum.
At extreme FFDI values increased suppression effort and decreased response time resulted in the greatest effect on the probability of containment within 5 ha, whereas fuel treatment had a stronger influence on fire size and distance travelled (Figs. 2, 4 and 5). When fires are small (
